For sequence classification, an important issue is to find discriminative features, where
sequential pattern mining (SPM) is often used to find frequent patterns from sequences as …

The extraction of patterns displaying significant association with a class label is a key data
mining task with wide application in many domains. We study a variant of the problem that …

We study the problem of efficiently mining statistically-significant sequential patterns from
large datasets, under different null models. We consider one null model presented in the …

The identification of the set of k most central nodes of a graph, or centrality maximization, is a
key task in network analysis, with various applications ranging from finding communities in …

Pattern mining is an essential part of knowledge discovery and data analytics. It is a
powerful paradigm, especially when combined with constraint reasoning. In this overview …

Sequential pattern mining remains a challenging task due to the large number of redundant
candidate patterns and the exponential search space. In addition, further analysis is still …

We introduce a novel class of null models for the statistical validation of results obtained
from binary transactional and sequence datasets. Our null models are Row-Order Agnostic …

We describe Statistically-sound Knowledge Discovery from Data (StatKDD), a
groundbreaking change of paradigm that shifts the focus of the KDD pipeline from the …

We introduce novel null models for assessing the results obtained from observed binary
transactional and sequence datasets, using statistical hypothesis testing. Our null models …

“Sim Sala Bim!”—Silvan, https://en. wikipedia. org/wiki/Silvan_ (illusionist) Betweenness
centrality is a popular centrality measure with applications in several domains and whose …